New features added

PriceIndices/price_indicators.py

@@ -1,16 +1,13 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
 import warnings
 warnings.filterwarnings('ignore')
 
 
-class Indices(object):
+class Indices:
 
-    def __abs__(self):
-
-    def get_bvol_index(self,  price_data):
+    def get_bvol_index(price_data):
 
         """
          Volatility Index is a measure of market's expectation of volatility over the near term.
@@ -22,6 +19,7 @@ def get_bvol_index(self,  price_data):
           :param price_data: pandas DataFrame
           :return: pandas DataFrame
           """
+
         try:
             df = price_data
             df = df.sort_values(by='date').reset_index(drop=True)
@@ -35,12 +33,12 @@ def get_bvol_index(self,  price_data):
         except Exception as e:
             return e
 
-    def get_bvol_graph(self, bvol_data):
+    def get_bvol_graph(bvol_data):
 
         """Make a line graph of bvol index with respect to time"""
         try:
             df = bvol_data
-            fig, ax = plt.subplots(figsize=(16, 12))
+            fig, ax = plt.subplots(figsize=(14, 12))
             rect = fig.patch
             rect.set_facecolor('yellow')
             ax1 = plt.subplot(211)
@@ -57,19 +55,18 @@ def get_bvol_graph(self, bvol_data):
             plt.ylabel('Volatility Index', color='r', fontsize=20)
             plt.legend()
             plt.setp(ax2.xaxis.get_majorticklabels(), rotation=90)
-            ax2.xaxis.set_major_locator(ticker.MultipleLocator(30))
             ax2.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)
 
             ax2.tick_params(axis='x', colors='b')
             ax2.tick_params(axis='y', colors='b')
 
             plt.suptitle('Price  and  Volatility Index', color='red', fontsize=24)
-            plt.savefig('bvol_index.png')
+            plt.savefig('bvol_index.png',bbox_inches='tight', facecolor='orange')
             return plt.show()
         except Exception as e:
             return  e
 
-    def get_rsi(self, price_data):
+    def get_rsi(price_data):
 
         """
         Type:
@@ -91,7 +88,7 @@ def get_rsi(self, price_data):
         """
         try:
             df = price_data
-            df['price_change'] = (df['price'] - df['price'].shift(1))
+            df['price_change'] = df['price'] - df['price'].shift(1)
             df = df.dropna()
             df['gain'] = df['price_change'].apply(lambda x: x if x >= 0 else 0)
 
@@ -118,7 +115,7 @@ def get_rsi(self, price_data):
     def get_rsi_graph(rsi_data):
         try:
             df = rsi_data
-            fig, ax = plt.subplots(figsize=(16, 12))
+            fig, ax = plt.subplots(figsize=(14, 12))
             rect = fig.patch
             rect.set_facecolor('yellow')
             ax1 = plt.subplot(211)
@@ -132,11 +129,10 @@ def get_rsi_graph(rsi_data):
             ax2.plot(df['date'], df['RSI_2'], color='b', label='RSI')
             plt.xlabel('Time', color='red', fontsize=20)
             plt.ylabel('Relative Strength Index (RSI)', color='r', fontsize=20)
-            plt.text('2019-06-01', 71.5, '>70 OverBought', fontsize=20, color='green')
-            plt.text('2019-06-01', 23, '<30 OverSold', fontsize=20, color='green')
+            plt.text(df['date'][int(len(df)/2)], 80, '>70 OverBought', fontsize=20, color='black')
+            plt.text(df['date'][int(len(df)/2)], 15, '<30 OverSold', fontsize=20, color='black')
             plt.legend()
             plt.setp(ax2.xaxis.get_majorticklabels(), rotation=90)
-            ax2.xaxis.set_major_locator(ticker.MultipleLocator(30))
 
             ax2.tick_params(axis='x', colors='b')
             ax2.tick_params(axis='y', colors='b')
@@ -145,12 +141,12 @@ def get_rsi_graph(rsi_data):
             ax2.axhline(y=30, color='r')
 
             plt.suptitle('Price  and  Relative  Strength Index', color='red', fontsize=24)
-            plt.save('rsi.png')
-            return plot.show()
+            plt.savefig('rsi.png', bbox_inches='tight', facecolor='orange')
+            return plt.show()
         except Exception as e:
             return e
 
-    def get_bollinger_bands(self, price_data, days=20):
+    def get_bollinger_bands(price_data, days=20):
         """
         Type:
             Trend, volatility, momentum indicator
@@ -178,7 +174,7 @@ def get_bollinger_bands(self, price_data, days=20):
             df['pluse'] = df['SMA'] + df['SD']*2
             df['minus'] = df['SMA'] - df['SMA']*2
 
-            fig, ax = plt.subplots(figsize=(20, 16))
+            fig, ax = plt.subplots(figsize=(16, 12))
             plt.plot(df['date'], df['pluse'], color='g')
             plt.plot(df['date'], df['minus'], color='g')
             plt.plot(df['date'], df['price'], color='orange')
@@ -189,13 +185,13 @@ def get_bollinger_bands(self, price_data, days=20):
             plt.tick_params(labelsize =17)
             fig.set_facecolor('yellow')
             plt.grid()
-            plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='yellow')
+            plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='orange')
             plt.show()
             return df
         except Exception as e:
             return e
 
-    def moving_average_convergence_divergence(self, price_data):
+    def get_moving_average_convergence_divergence(price_data):
         """
         Type
             Trend and momentum indicator
@@ -212,10 +208,19 @@ def moving_average_convergence_divergence(self, price_data):
         :param price_data: pandas DataFrame
         :return:
         """
+        try:
+            df = price_data
+            df['SMA_12'] = df['price'].rolling(12).mean()
+            df['SMA_26'] = df['price'].rolling(26).mean()
+            df['MACD'] = df['SMA_12'] - df['SMA_26']
+            df = df.dropna()
+            return df
+        except Exception as e:
+            return print('MACD Error - {}'.format(e))
 
 
 
-    def simple_moving_average(self, price_data, days):
+    def get_simple_moving_average(price_data, days):
         """
         Simple moving average of given days
         :param price_data: pandas DataFrame
@@ -225,6 +230,7 @@ def simple_moving_average(self, price_data, days):
         try:
             df = price_data
             df['SMA'] = df['price'].rolling(days).mean()
+            df = df.dropna()
             return df
         except Exception as e:
             return print('SMA Error - {}'.format(e))

README.md

@@ -25,8 +25,8 @@ from PriceIndices import MarketHistory, Indices
 ```python
 >>> history = MarketHistory()
 
->>> df = history.get_history('bitcoin', '20130428', '20190624')  # Get Market History
->>> df.head()
+>>> df_history = history.get_history('bitcoin', '20130428', '20190624')  # Get Market History
+>>> df_history.head()
         Date     Open*      High       Low   Close**       Volume    Market Cap
 0 2019-06-23  10696.69  11246.14  10556.10  10855.37  20998326502  192970090355
 1 2019-06-22  10175.92  11157.35  10107.04  10701.69  29995204861  190214124824
@@ -35,9 +35,9 @@ from PriceIndices import MarketHistory, Indices
 4 2019-06-19   9078.73   9299.62   9070.40   9273.52  15546809946  164780855869
 
 
->>> df =  history.get_price('bitcoin', '20130428', '20190624')  # Get closing price
+>>>price_data  =  history.get_price('bitcoin', '20130428', '20190624')  # Get closing price
 
->>> df.head()
+>>> price_data .head()
         date     price
 0 2019-06-23  10855.37
 1 2019-06-22  10701.69
@@ -46,7 +46,7 @@ from PriceIndices import MarketHistory, Indices
 4 2019-06-19   9273.52
 
 
->>> df_bvol = Indices.get_bvol_index(df)  # Calculate Volatility Index
+>>> df_bvol = Indices.get_bvol_index(price_data )  # Calculate Volatility Index
 >>> df_bvol.head()
         date     price  BVOL_Index
 0 2019-06-22  10701.69    0.636482
@@ -60,8 +60,12 @@ from PriceIndices import MarketHistory, Indices
 """
 This will return a plot of BVOL index against time also save volatility index plot in your working directory as 'bvol_index.png'
 """
+```
+![](plots/bvol_index.png)
+
+```python
 
->>> df_rsi = Indices.get_rsi(df)   # Calculate RSI
+>>> df_rsi = Indices.get_rsi(price_data)   # Calculate RSI
 
 >>> print(df_rsi.tail())
            date   price  price_change   gain   loss  gain_average  loss_average        RS      RSI_1  RS_Smooth      RSI_2
@@ -76,7 +80,10 @@ This will return a plot of BVOL index against time also save volatility index pl
 """
 This will return a plot of RSI against time and also save RSI plot in your working directory as 'rsi.png'
 """
->>> df_bb = Indices.get_bollinger_bands(df, 20) # Get Bollinger Bands and plot
+```
+![](plots/rsi.png)
+```python
+>>> df_bb = Indices.get_bollinger_bands(price_data , 20) # Get Bollinger Bands and plot
 >>> df_bb.tail()
            date   price       SMA         SD       pluse     minus
 2243 2013-05-02  105.21  115.2345   6.339257  127.913013 -115.2345
@@ -90,7 +97,33 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
 """
 
 ```
+![](plots/bollinger_bands.png)
+
+```python
+
+>>> df_macd= Indices.get_moving_average_convergence_divergence(price_data ) # Get moving average convergence divergence 
+
+>>> df_macd.tail()
+           date   price      SMA_12      SMA_26      MACD
+2257 2013-05-02  105.21  112.235833  118.603077 -6.367244
+2258 2013-05-01  116.99  112.153333  118.112692 -5.959359
+2259 2013-04-30  139.00  114.153333  118.325000 -4.171667
+2260 2013-04-29  144.54  116.595000  118.808077 -2.213077
+2261 2013-04-28  134.21  118.012500  118.846923 -0.834423
+
+```
+```python
+>>> df_sma = Indices.get_simple_moving_average(price_data,20) # Get simple moving average
+>>> df_sma.tail()
+           date   price       SMA
+2257 2013-05-02  105.21  115.2345
+2258 2013-05-01  116.99  114.9400
+2259 2013-04-30  139.00  115.7900
+2260 2013-04-29  144.54  116.9175
+2261 2013-04-28  134.21  117.4530
+
 
+```
 ### License 
 [MIT](https://choosealicense.com/licenses/mit/) © [Dayal Chand Aichara](https://github.com/dc-aichara)